Publications

Abstract

This paper addresses the task of efficient object class detection by means of
the Hough transform. This approach has been made popular by the Implicit Shape
Model (ISM) and has been adopted many times. Although ISM exhibits robust
detection performance, its probabilistic formulation is unsatisfactory. The
PRincipled Implicit Shape Model (PRISM) overcomes these problems by
interpreting Hough voting as a dual implementation of linear sliding-window
detection. It thereby gives a sound justification to the voting procedure and
imposes minimal constraints. We demonstrate PRISM's flexibility by two
complementary implementations: a generatively trained Gaussian Mixture Model as
well as a discriminatively trained histogram approach. Both systems achieve
state-of-the-art performance. Detections are found by gradient-based or branch
and bound search, respectively. The latter greatly benefits from PRISM's
feature-centric view. It thereby avoids the unfavourable memory trade-off and
any on-line pre-processing of the original Efficient Subwindow Search (ESS).
Moreover, our approach takes account of the features' scale value while ESS
does not. Finally, we show how to avoid soft-matching and spatial pyramid
descriptors during detection without losing their positive effect. This makes
algorithms simpler and faster. Both are possible if the object model is
properly regularised and we discuss a modification of SVMs which allows for
doing so.